MétaCan
Menu
Back to cohort

AB1328 MEASUREMENT PROPERTIES OF THE BRIEF PAIN INVENTORY-SHORT FORM (BPI-SF) AND THE REVISED SHORT-FORM MCGILL PAIN QUESTIONNAIRE-VERSION-2 (SF-MPQ-2) IN PAIN-RELATED MUSCULOSKELETAL CONDITIONS: A SYSTEMATIC REVIEW

2019· review· en· W2949691522 on OpenAlexaffabout
Joy C. MacDermid, Samuel U. Jumbo, Michael Kalu, Tara Packham, George S. Athwal, Ken Faber

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2019
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster UniversityWestern University
Fundersnot available
KeywordsBrief Pain InventoryMedicineCINAHLMcGill Pain QuestionnairePhysical therapyInterpretabilityQuality of life (healthcare)Reliability (semiconductor)Musculoskeletal painChronic painPsychiatryArtificial intelligenceVisual analogue scaleComputer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.013
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.083
GPT teacher head0.321
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2019
Admission routes2
Has abstractno

Explore more

Same venueAnnals of the Rheumatic DiseasesSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207